Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad Campus

This study investigates the influence of Japan's Monetary Policy Uncertainty (MPU) on Asian developed, emerging, and frontier stock markets from January 2006 to December 2022 using the Quantile-on-Quantile regression. The findings reveal a negative impact of Japan's MPU on stock returns across various countries. However, the strength of this effect varies, underscoring differences in economic policy uncertainty spillover. Notably, the study identifies that the negative impact is more pronounced at higher MPU quantiles and lower stock market return quantiles. Conversely, positive impacts are observed at lower MPU quantiles and higher stock market return quantiles, suggesting that increased uncertainty diminishes returns during bearish trends and enhances returns during bullish trends. Frontier markets exhibit a neutral to positive relationship, with the positive effect more noticeable during bearish conditions and across different MPU quantiles. The robustness of these results has been verified by using the Instrumental Variable Quantile regression (IVQR) model. The findings emphasize the need for policymakers to enhance transparency and communication to mitigate the adverse effects of Japan's MPU on Asian stock markets.

This study employs asymmetric quantile regression to investigate the asymmetric impact of WTI crude oil prices and economic policy uncertainty (EPU) on stock market returns from May 2014 to December 2024 in oil-importing (China, India, Germany, Italy, Japan, USA, and South Korea) and oil-exporting (Saudi Arabia, Russia, Iraq, Canada, and the United Arab Emirates) countries. The findings reveal that an increase in oil prices significantly impacts the returns of all countries. For oil-importing countries, an increase in oil prices consistently exhibits a positive impact, with insignificant effects in lower and medium quantiles and significant effects in higher quantiles. Conversely, a decrease in oil prices generally decreases stock market returns across all quantiles. This study offers valuable insights for investors to manage risks and improve the predictability of oil price fluctuations. It also provides strategies and policy implications for capitalists and decision-makers. By addressing contemporary issues and using up-to-date data, the study supports financial institutions and portfolio managers in formulating effective strategies.

Purpose High-Mobility Group A1 (HMGA1) is a chromatin-associated protein involved in regulating key cellular processes, including DNA transcription, replication, recombination, and repair. It is highly expressed during embryogenesis and reactivated in various cancers, where it contributes to tumor progression and metastasis. We investigated the prognostic significance of HMGA1 gene expression in gliomas by comparing its expression in normal brain tissue and different glioma grades. Methods Real-time quantitative PCR (qPCR) was performed on 75 glioma samples obtained from Aga Khan University Hospital (Pakistan), along with 10 Normal Adjacent Tissue (NAT) samples. The correlation between HMGA1 expression and prognosis was evaluated using Kaplan–Meier (KM) plotter in glioma patients. Statistical analyses were conducted using the R platform and further validated through the online database Chinese Glioma Genome Atlas (CGGA) using online tools. Results HMGA1 expression was significantly upregulated in gliomas compared to NAT (p < 0.001) and increased with tumor grade (p = 0.015). High HMGA1 expression correlated with Ki-67 levels and was associated with worse survival (p = 0.0014). Patients with elevated HMGA1 had a 3.5-fold higher mortality risk (95% CI: 1.5–7.9, p = 0.003). ROC analysis yielded an AUC of 0.752, indicating its potential prognostic value. Conclusion HMGA1 overexpression is associated with poor prognosis in gliomas, suggesting its potential as a prognostic marker. However, further validation is needed to confirm its clinical utility.

This study examined the impact of Customer Knowledge Management (CKM) and the Role of Management Involvement on the success of Change Projects within organizations, particularly focusing on Pakistan’s software industry. Using a quantitative research approach and data from 280 employees, the findings revealed that while management's role has a significant positive effect on change project success, CKM does not show a direct positive influence. The research extends the theoretical understanding of Dynamic Capabilities Theory by emphasizing internal leadership behaviors over information systems in change initiatives. Practical recommendations include prioritizing leadership development and better aligning CKM efforts with organizational change goals. The study highlights limitations such as sector-specific focus and cross-sectional design, and suggests future research incorporate broader industries, longitudinal methods, and mediating factors like organizational culture.

Alignment of advanced cutting-edge technologies such as Artificial Intelligence (AI) has emerged as a significant driving force to achieve greater precision and timeliness in identifying cardiovascular diseases (CVDs). However, it is difficult to achieve high accuracy and reliability in CVD diagnostics due to complex clinical data and the selection and modeling process of useful features. Therefore, this paper studies advanced AI-based feature selection techniques and the application of AI technologies in the CVD classification. It uses methodologies such as Chi-square, Info Gain, Forward Selection, and Backward Elimination as an essence of cardiovascular health indicators into a refined eight-feature subset. This study emphasizes ethical considerations, including transparency, interpretability, and bias mitigation. This is achieved by employing unbiased datasets, fair feature selection techniques, and rigorous validation metrics to ensure fairness and trustworthiness in the AI-based diagnostic process. In addition, the integration of various Machine Learning (ML) models, encompassing Random Forest (RF), XGBoost, Decision Trees (DT), and Logistic Regression (LR), facilitates a comprehensive exploration of predictive performance. Among this diverse range of models, XGBoost stands out as the top performer, achieving exceptional scores with a 99% accuracy rate, 100% recall, 99% F1-measure, and 99% precision. Furthermore, we venture into dimensionality reduction, applying Principal Component Analysis (PCA) to the eight-feature subset, effectively refining it to a compact six-attribute feature subset. Once again, XGBoost shines as the model of choice, yielding outstanding results. It achieves accuracy, recall, F1-measure, and precision scores of 98%, 100%, 98%, and 97%, respectively, when applied to the feature subset derived from the combination of Chi-square and Forward Selection methods.

The purpose of this study is to investigate the responses of sector economic activity of the Japanese stock market to Economic Policy Uncertainty (EPU). To investigate this relationship, we take monthly data covering ten sectors of Japan’s economy and the EPU index spanning from January 2000 to January 2024. For the empirical analysis, we used a recently introduced approach, namely Cross-Quantilogram (CQ), and Quantile-on-Quantile Regression (QQR) for the robustness of the estimation output. Our findings indicate that EPU transmits negative and positive shocks to the Japanese sectors from bearish to bullish market states. Surprisingly, at the bearish state, we find that sector stocks respond negatively to the higher quantiles of EPU under short memory. Moreover, we also observed that EPU transmits a weak positive signal to sectors at medium quantiles. Similarly, we report a less pronounced effect of EPU on different sectors considering different memories (quarterly, bi-annual, and annual). Furthermore, our findings indicate that some sectors could serve as diversifiers in normal market conditions and are considered to be safe-haven against the EPU in bearish periods of economic activity. Our research has profound implications for portfolio managers, policy makers, and investors in terms of ensuring proactive strategies and regulatory measures.

This study aims to identify stock price jumps and examine stock returns dynamics in emerging Asian stock markets. Additionally, the study employs the Swap Variance estimation approach to measure integrated volatility and determine monthly jumps. Specifically, three diverse approaches are used to identify monthly integrated volatility: realized volatility, bi-power variations, and tri-power variations, using daily data from February 1, 2001, to October 30, 2022. The findings of the study indicate that jumps occur in all markets, leading to increased market volatility. However, jumps are more frequent, particularly during the Global Financial Crisis of 2008 and the COVID-19 periods, which significantly amplify the overall volatility of emerging Asian equity markets. Furthermore, positive jumps are more common than negative jumps, although negative jumps tend to have a greater magnitude than positive jumps. Moreover, abnormal returns are higher during jump periods compared to non-jump periods. Negative jumps particularly impact stock markets where returns are lower and volatility is higher on regular days. However, stock markets with moderate volatility during regular days tend to earn higher returns in jump periods. Finally, when negative jumps occur, the integrated volatility is higher compared to positive jumps, and this trend is observed across all markets. The findings of this study offer valuable insights for policymakers, portfolio managers, and stock market investors.

OBJECTIVE: To explore the potential of combining natural herbs like chamomile and saffron for the management of anxiety and depression. METHODS: A rodent model of Major Depressive Disorder (MDD) and anxiety, secondary to streptozotocin-induced diabetes mellitus was made. A total of 6 rat groups were chosen; healthy and diseased controls; and diseased test groups of fluoxetine, saffron, chamomile, and combined saffron and chamomile treated (n = 6/group). Activity by forced swim test (FST), elevated plus maze test (EPMT), and correlations with biochemical markers like serum glucose, tryptophan, C-reactive protein (CRP), brain derived neurotrophic factor (BDNF) and 5-hydrox-ytryptamine 2C receptor (5HT2CR) expression, were assessed at the end of the 3rd week of the treatment. A one-way analysis of variance with a post-hoc Tukey's test was applied. RESULTS: The combined herbal treatment group showed significantly better (P < 0.05) than all other groups in terms of anti-hyperglycemic effect. All treatments improved the CRP levels; however, the combination group was also significantly better than fluoxetine and the individual herb groups. Only the herb groups showed efficacy in the FST with added benefits of the combination group over the healthy controls and similar trends in the EPMT. However, expression of 5HT2CR was repressed while BDNF was elevated through treatment. CONCLUSION: This study shows that in comparison to treatment with a SSRI, and individual herbs, the combination of chamomile and saffron showed overall improved outcomes.

The much-anticipated metaverse will fundamentally transform how consumers interact with machines across smartX environments. Information overload is a problem that Metaverse users can encounter when they navigate a myriad of multimedia resources. Providing effective and timely categorization of resources can facilitate effective human-human communications in Metaverse. Existing categorization techniques for current web have four key limitations: segregation of feature selection and feature correlation, validation using small datasets, lack of comparative analysis of multilabel classifiers, and lack of framework for selecting optimal classifiers. These limitations hinder existing methods' applicability to Metaverse. In response, this paper presents an effective framework for multilabel categorization of Metaverse resources using natural language processing and machine learning. The proposed framework comprises several major steps including extraction of contents, preprocessing, removing redundant and ambiguous data, converting text into features, then feature selection and classification. The proposed framework has been implemented as a predictive model for Metaverse users and tested on benchmark and real-world datasets. Experimental evaluation is conducted based on multiple parameters: accuracy, average precision, Hamming loss (HLoss), one error (1Error), ranking loss (RLoss). Experimental results show maximum increase in accuracy of 10% for both datasets. Comparative analysis indicates the proposed framework outperforms existing models not only by accuracy but also with advanced evaluation measures such as Hamming loss, ranking loss, and one error

The integration of vision and motion systems represents a critical phase in the intelligent transformation of consumer electronics aimed at enhancing productivity. However, owing to the operational modes and distributional characteristics inherent in existing systems, achieving large-scale, stable, and consistent agricultural applications on consumer electronics remains a significant challenge. To address this problem, this paper proposes a unified resource optimization approach for different configurations of agricultural consumer electronics to achieve deep integration of vision and motion systems. The optimization is mainly at two levels: on the one hand, we design a velocity observer for the vision-motion integrated system represented by the visual servoing system, which makes native changes to the characteristics of the visual servoing’s non-real-time instruction issuance. By converting long-time commands with uncertain period to short-time commands with certain period, the design difficulty of real-time trajectory planning of the real underlying motion controller is simplified. On the other hand, in image-based visual servoing (IBVS) system, the mixture parameter of the image Jacobian matrix also affect the control performance of the visual servoing system. For most IBVS-based agricultural applications, there is a lack of a systematic approach to ensure that the mixture parameter is adaptively and continuously varied. To solve this problem, this paper proposes a fuzzy logic-based method to adaptively adjust this parameter and ensure its continuity by introducing a suitable membership function. The experimental results of visual servoing based on the consumer electronics show that our proposed method can significantly improve the integrated vision-motion controllability, and can trade-off the convergence efficiency and feature retention constraints to effectively improve the overall efficiency of the system operation.

In the present study, Pseudomonas aeruginosa AAC1 was utilized to design a phosphorus-rich compost. Physicochemical and Illumina MiSeq sequencing were employed to explore the effect of inoculation on the microbial diversity in inoculated pile, which was compared with an un-inoculated compost pile. The findings indicate that compost maturity was achieved by 60th day, characterized by optimal temperature (28 °C), pH (7.1—7.5), and moisture content (32% – 38%). The relative abundance of P. aeruginosa decreased in mature compost, coinciding with a notable increase in the available phosphorus level, reaching up to 6.82 g kg−1 in inoculated compost piles. The addition of P. aeruginosa AAC1 did not influence the C, N, C: N ratio, organic matter, and ash content but significantly decreased the pH and moisture contents and increased the composting temperature, available phosphorus, calcium, and magnesium content. The inoculation of Pseudomonas aeruginosa AAC1 in compost did not significantly alter the structure of the microbial community, since metagenomic analysis revealed similar microbial communities in inoculated and un-inoculated compost piles. Overall, the dominated bacterial genera were Actinobacteria, Proteobacteria, Bacteroidetes, and Firmicutes at the phylum level whereas, the fungal communities were densely populated with Ascomycota, Aphelidiomycota, Chytridiomycota, and Cercozoa. This study confirmed the potential of Pseudomonas aeruginosa AAC1 as a phosphatic bioinoculant by ameliorating the physiochemical conditions of compost without compromising the microbial diversity of compost.

In this study, the fabrication of magnetic hemicellulosic composite microspheres and the adsorption of copper ions are explored. The microspheres were prepared by the micro-emulsion technique, using Fe3O4 nanoparticles and hemicellulose extracted from wheat straw with the ionic liquid B[mim]Cl as a solvent. Fe3O4 nanoparticles, synthesized through coprecipitation, were evenly encapsulated within the hemicellulosic microspheres. The Fe3O4 nanoparticles measured 10–15 nm in size, while the microspheres had an average diameter of about 20 μm and displayed a saturation magnetization of 35.95 emu/g. The optimal conditions for copper adsorption by the microspheres were found to be a pH of 5.0, a temperature of 323 K, and an initial copper ion concentration of 80 mg/L, resulting in an adsorption capacity of 85.65 mg/g after 24 h. The adsorption kinetics followed a pseudo-second-order model, and the Langmuir isotherm suggested a monomolecular layer adsorption mechanism, with a theoretical maximum capacity of 149.25 mg/g. In summary, the magnetic hemicellulosic microspheres exhibited considerable adsorption potential and favorable recycling capabilities for copper ions.

Smart fish farming faces critical challenges in achieving comprehensive automation, real-time decision-making, and adaptability to diverse environmental conditions and multi-species aquaculture. This study presents a novel Internet of Things (IoT)-driven intelligent decision-making system that dynamically monitors and optimizes water quality parameters to enhance fish survival rates across various regions and species setups. The system integrates advanced sensors connected to an ESP32 microcontroller, continuously monitoring key water parameters such as pH, temperature, and turbidity which are increasingly affected by climate-induced variability. A custom-built dataset comprising 43,459 records, covering ten distinct fish species across diverse pond environments, was meticulously curated. The data were stored as a comma-separated values (CSV) file on the IoT cloud platform ThingSpeak and synchronized with Firebase, enabling seamless remote access, control, and real-time updates. Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. Our framework’s capability to manage complex, multi-species fishpond environments was validated across diverse setups, showcasing its potential to transform fish farming practices by ensuring sustainable climate-adaptive management through real-time water quality optimization. This study marks a significant step forward in climate-smart aquaculture, contributing to enhanced fish health, survival, and yield while mitigating the risks posed by climate change on aquatic ecosystems.

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business failure in Brazil by employing advanced machine learning techniques. We collected data from 235 companies and conducted principal component analysis (PCA) on 40 variables already used in the bankruptcy failure literature, resulting in the formation of seven variables that predict business failure. The results indicate that ESG factors significantly predict business failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool for risk assessment and strategic decision-making.

INTRODUCTION The use of electronic cigarettes (ECs) has surged globally, particularly among young individuals. This study aimed to assess the perceptions of vaping-related oral health risks, clinical oral health status, and self-perceived dental and periodontal conditions among young adult users of ECs in Pakistan. METHODS A cross-sectional study was conducted from June 2023 to March 2024, recruiting 142 young users of ECs. Intraoral examinations assessed Decayed, Missing, and Filled Teeth (DMFT) index, Oral Hygiene Index Simplified (OHI-S), Gingival Bleeding Index (GBI), Plaque Index (PI), and dental stain. Data on sociodemographic characteristics, oral health behaviors, vaping habits, and perceptions of impact of vaping on oral health were gathered through a self-administered questionnaire. Associations between EC use and various oral health variables were analyzed using the chi-squared and Fisher’s exact tests. RESULTS Mean DMFT was 5.66 (SD=2.20). Poor oral hygiene (29.6%) and severe dental staining were prevalent. Most participants (76.1%) brushed once daily, while only 34.5% attended regular dental check-ups. Gingival bleeding and plaque accumulation were observed in 47.2% and 35.3% of participants. Around 66% reported daily EC use, with 80.3% initiating vaping before the age of 18 years. Most common reason for vaping was perception that ECs are safer than traditional smoking (31.7%). Participants' perceptions of vaping-related oral health risks were relatively low, with 45% associating vaping with tooth decay, 48% with gum disease, and 58.5% with tooth staining. Tooth brushing frequency, vaping frequency (per day), and time since vaping started, were significantly associated with oral clinical indicators (p<0.05). The education level was the only variable significantly associated with vaping-related risk perception (p<0.05). CONCLUSIONS The study reveals that oral health awareness among young vapers is low, with many starting EC use at a young age and exhibiting poor oral health behaviors. Misconceptions about the safety of ECs compared to conventional cigarettes may contribute to increased vaping.

Introduction Patients with diabetes require healthcare and information that are accurate and extensive. Large language models (LLMs) like ChatGPT herald the capacity to provide such exhaustive data. To determine (a) the comprehensiveness of ChatGPT's responses in Urdu to diabetes-related questions and (b) the accuracy of ChatGPT's Urdu responses when compared to its English responses. Methods A cross-sectional observational study was conducted. Two reviewers experienced in internal medicine and endocrinology graded 53 Urdu and English responses on diabetes knowledge, lifestyle, and prevention. A senior reviewer resolved discrepancies. Responses were assessed for comprehension and accuracy, then compared to English. Results Among the Urdu responses generated, only two of 53 (3.8%) questions were graded as comprehensive, and five of 53 (9.4%) were graded as correct but inadequate. We found that 25 of 53 (47.2%) questions were graded as mixed with correct and incorrect/outdated data, the most significant proportion of responses being graded as such. When considering the comparison of response scale grading the comparative accuracy of Urdu and English responses, no Urdu response (0.0%) was considered to have more accuracy than English. Most of the Urdu responses were found to have an accuracy less than that of English, an overwhelming majority of 49 of 53 (92.5%) responses. Conclusion We found that although the ability to retrieve such information about diabetes is impressive, it can merely be used as an adjunct instead of a solitary source of information. Further work must be done to optimize Urdu responses in medical contexts to approximate the boundless potential it heralds.

Objective To evaluate the effectiveness of Azadirachta indica based Herbal mouthwash to treat tooth sensitivity in patients. Method This single-blinded clinical trial was performed at School of dentistry, Shaheed Zulfiqar Ali Bhutto Medial University, Islamabad from 1st February, 2023 to 30th April, 2023. In this interventional study incorporated 120 participants with clinically visible signs of erosion, abrasion or recession. Visual Analog Scoring (VAS) Tool was used to investigate tooth sensitivity in these patients. Values of VAS for tooth sensitivity was calculated by exposing teeth of these patients to cold air blasting with triple syringe attached to dental unit at psi-30.0 pressure between 23±30ºC for duration of one second without using Azadirachta indica based Herbal mouthwash. Later on, these patients were provided with this Herbal mouthwash and its usage was recommended twice a day for one month. After One month, tooth sensitivity of patients was determined by VAS again. Data was analyzed by Paired T-test at 95% confidence and significance < 0.05. Results VAS mean value for tooth sensitivity of patients before using Azadirachta indica based Herbal mouthwash was higher and found to be 55.43% ± 12.04 whereas its mean value after using Herbal mouthwash for one month reduced to 35.38% ± 11.62 which was statistically significant (p value=0.001). Reduction in tooth sensitivity of patients was almost 20.05% just after one month. Conclusion Azadirachta indica based Herbal mouthwash was potent enough to reduce the tooth sensitivity in patients after one month of its usage.